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Record W2063560253 · doi:10.1071/zo12075

Genetic diversity in natural and introduced island populations of koalas in Queensland

2013· article· en· W2063560253 on OpenAlexaboutno aff
Kristen E. Lee, Jennifer M. Seddon, Stephen D. Johnston, Sean FitzGibbon, Frank Carrick, Alistair Melzer, Fred B. Bercovitch, William Ellis

Bibliographic record

VenueAustralian Journal of Zoology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEcologySpecies richnessGenetic diversityPopulationFounder effectInbreedingMainlandZoologyHaplotypeAlleleDemography

Abstract

fetched live from OpenAlex

Island populations of animals are expected to show reduced genetic variation and increased incidence of inbreeding because of founder effects and the susceptibility of small populations to the effects of genetic drift. Koalas (Phascolarctos cinereus) occur naturally in a patchy distribution across much of the eastern Australian mainland and on a small number of islands near the Australian coast. We compared the genetic diversity of the naturally occurring population of koalas on North Stradbroke Island in south-east Queensland with other island populations including the introduced group on St Bees Island in central Queensland. The population on St Bees Island shows higher diversity (allelic richness 4.1, He = 0.67) than the North Stradbroke Island population (allelic richness 3.2, He = 0.55). Koalas on Brampton, Newry and Rabbit Islands possessed microsatellite alleles that were not identified from St Bees Island koalas, indicating that it is most unlikely that these populations were established by a sole secondary introduction from St Bees Island. Mitochondrial haplotypes on the central Queensland islands were more similar to a haplotype found at Springsure in central Queensland and the inland clades in south-east Queensland, rather than the coastal clade in south-east Queensland.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.233
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2013
Admission routes1
Has abstractyes

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